betweenness.centrality.clustering {RBGL} | R Documentation |
Graph clustering based on edge betweenness centrality
betweenness.centrality.clustering(g, threshold = -1, normalize = T))
g |
an instance of the graph class with edgemode
“undirected” |
threshold |
threshold to terminate clustering process |
normalize |
boolean, when true, the threshold is compared with the normalized edge centrality based on the input graph; when false, the threshold is compared with the absolute edge centrality |
To implement graph clustering based on edge betweenness centrality. The algorithm is iterative, at each step it computes the edge betweenness centrality and removes the edge with the maximum betweenness centrality. See documentation on Clustering algorithms in Boost Graph Library for details.
A list of
betweenness.centrality.vertices |
betweenness centrality of each vertex |
betweenness.centrality.edges |
betweenness centrality of each edge |
relative.betweenness.centrality.vertices |
relative betweenness centrality of each vertex |
dominance |
central point dominance |
Li Long <li.long@isb-sib.ch>
Boost Graph Library by Siek et al.
coex <- fromGXL(file(system.file("XML/conn.gxl",package="RBGL"))) coex@edgemode <- "undirected" betweenness.centrality.clustering(coex)